基于遗传模拟退火算法的电梯轿厢生产车间调度研究

发布时间：2018-01-11 08:10

本文关键词：基于遗传模拟退火算法的电梯轿厢生产车间调度研究　出处：《广东工业大学》2011年硕士论文　论文类型：学位论文

【摘要】：车间作业调度问题既是制造系统实际生产的重要问题,也是理论研究的难点之一。该问题是生产管理的核心问题,有效的车间作业调度方法的研究和应用,对于制造企业提高产品质量、降低生产成本、提高生产效率等方面起着至关重要的最用,越来越受到学者们的关注。本文以中山某五金厂为背景,以电梯轿厢生产为研究对象,研究了电梯轿厢生产车间调度策略。针对电梯轿厢生产过程的特点,进行了数学抽象化描述,建立了该问题的数学模型。针对电梯轿厢生产车间调度问题的特性,为了避免传统遗传算法存在过早收敛及后期搜索效率低等问题,采用了阶段进化的自适应遗传模拟退火算法,并详细分析了算法的设计过程。针对不同工件有不同工序数这一特点,对传统的基于工序的编码方法进行了改进。运用MATLAB编程对算例进行了仿真,将改进编码方法的阶段进化的遗传模拟退火算法求解的结果同改进编码方法的遗传算法、传统的阶段进化的遗传模拟退火算法和传统编码方法的遗传模拟退火算法进行比较,显示了本文改进编码方法的阶段进化的遗传模拟退火算法求解调度模型的优越性。在电梯轿厢生产过程中,不同的工件有不同的工序数,每一道加工工序上存在并行机且选择不同的机器其加工时间不同,由此电梯轿厢生产调度问题不仅需要找到最佳的排序方案,还要选择最合适的机器。因此在改进编码方法的基础上采用了双层编码,第一层为工件安排的加工顺序,第二层为选择的机器。然后,对该编码方法的阶段进化的遗传模拟退火算法在电梯轿厢生产车间作业调度问题上的具体应用进行了详细的设计,主要包括：遗传操作(选择操作、交叉操作和变异操作)的设计、模拟退火操作的设计等等。最后,针对生产周期和提前/拖期惩罚的双目标调度模型,求解出电梯轿厢零部件生产的调度方案。
[Abstract]:An important issue in practical production system is not only the job shop scheduling problem, is also one of the difficulties of theoretic research. The problem is the core problem in production management, the research and application of job shop scheduling methods effectively, for manufacturing enterprises to improve product quality, reduce production cost, improve production efficiency play a crucial part, and more attention by scholars.
Based on the background of a hardware factory in Zhongshan, in the elevator car production as the research object, research on the job shop scheduling strategy of car production. According to the characteristics of elevator elevator car production process, the mathematical abstract description, established the mathematical model of the problem. According to the characteristics of the production scheduling problem of elevator students. In order to avoid the traditional genetic algorithm premature convergence and searching efficiency low, the evolutionary stage of adaptive genetic simulated annealing algorithm, and a detailed analysis of the design process of the algorithm. According to the different workpiece with the characteristics of different number of processes, the traditional encoding method based on process was improved. Simulated by MATLAB for example, the genetic algorithm with the improved encoding method of genetic simulated annealing algorithm for phase evolution will improve the encoding method of the results of the traditional genetic evolution stage The simulated annealing algorithm is compared with the traditional genetic algorithm simulated annealing algorithm, which shows the superiority of the improved genetic algorithm simulated annealing algorithm to solve the scheduling model.
In the elevator car production process, different parts have different number of processes, each process on existing parallel machines and choose different machine processing time is different, the elevator scheduling problem not only need to find the optimal ordering scheme, and choose the most suitable machine. It adopts double encoding based on improved encoding method, the processing sequence of the first layer of the workpiece arrangement, the second layer is the choice of the machine. Then, the detailed design, the specific application of genetic simulated annealing algorithm for the encoding stage of evolution method in the elevator car production workshop scheduling problem mainly include: genetic operation (selection operation, crossover and mutation) design, simulated annealing operation design and so on. Finally, according to the production cycle and the earliness / tardiness scheduling model of double objective punishment period, calculate the elevator A scheduling scheme for the production of parts and components.

【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186;TP18

【参考文献】